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# coding=utf-8

# Copyright 2022 HuggingFace Inc.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import tempfile

import unittest

from diffusers import (

DDIMScheduler,

DDPMScheduler,

DPMSolverMultistepScheduler,

EulerAncestralDiscreteScheduler,

EulerDiscreteScheduler,

PNDMScheduler,

logging,

)

from diffusers.configuration_utils import ConfigMixin, register_to_config

from diffusers.utils import deprecate

from diffusers.utils.testing_utils import CaptureLogger

class SampleObject(ConfigMixin):

config_name = "config.json"

@register_to_config

def __init__(

self,

a=2,

b=5,

c=(2, 5),

d="for diffusion",

e=[1, 3],

):

pass

class SampleObject2(ConfigMixin):

config_name = "config.json"

@register_to_config

def __init__(

self,

a=2,

b=5,

c=(2, 5),

d="for diffusion",

f=[1, 3],

):

pass

class SampleObject3(ConfigMixin):

config_name = "config.json"

@register_to_config

def __init__(

self,

a=2,

b=5,

c=(2, 5),

d="for diffusion",

e=[1, 3],

f=[1, 3],

):

pass

class ConfigTester(unittest.TestCase):

def test_load_not_from_mixin(self):

with self.assertRaises(ValueError):

ConfigMixin.load_config("dummy_path")

def test_register_to_config(self):

obj = SampleObject()

config = obj.config

assert config["a"] == 2

assert config["b"] == 5

assert config["c"] == (2, 5)

assert config["d"] == "for diffusion"

assert config["e"] == [1, 3]

# init ignore private arguments

obj = SampleObject(_name_or_path="lalala")

config = obj.config

assert config["a"] == 2

assert config["b"] == 5

assert config["c"] == (2, 5)

assert config["d"] == "for diffusion"

assert config["e"] == [1, 3]

# can override default

obj = SampleObject(c=6)

config = obj.config

assert config["a"] == 2

assert config["b"] == 5

assert config["c"] == 6

assert config["d"] == "for diffusion"

assert config["e"] == [1, 3]

# can use positional arguments.

obj = SampleObject(1, c=6)

config = obj.config

assert config["a"] == 1

assert config["b"] == 5

assert config["c"] == 6

assert config["d"] == "for diffusion"

assert config["e"] == [1, 3]

def test_save_load(self):

obj = SampleObject()

config = obj.config

assert config["a"] == 2

assert config["b"] == 5

assert config["c"] == (2, 5)

assert config["d"] == "for diffusion"

assert config["e"] == [1, 3]

with tempfile.TemporaryDirectory() as tmpdirname:

obj.save_config(tmpdirname)

new_obj = SampleObject.from_config(SampleObject.load_config(tmpdirname))

new_config = new_obj.config

# unfreeze configs

config = dict(config)

new_config = dict(new_config)

assert config.pop("c") == (2, 5) # instantiated as tuple

assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json

assert config == new_config

def test_load_ddim_from_pndm(self):

logger = logging.get_logger("diffusers.configuration_utils")

with CaptureLogger(logger) as cap_logger:

ddim = DDIMScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"

)

assert ddim.__class__ == DDIMScheduler

# no warning should be thrown

assert cap_logger.out == ""

def test_load_euler_from_pndm(self):

logger = logging.get_logger("diffusers.configuration_utils")

with CaptureLogger(logger) as cap_logger:

euler = EulerDiscreteScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"

)

assert euler.__class__ == EulerDiscreteScheduler

# no warning should be thrown

assert cap_logger.out == ""

def test_load_euler_ancestral_from_pndm(self):

logger = logging.get_logger("diffusers.configuration_utils")

with CaptureLogger(logger) as cap_logger:

euler = EulerAncestralDiscreteScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"

)

assert euler.__class__ == EulerAncestralDiscreteScheduler

# no warning should be thrown

assert cap_logger.out == ""

def test_load_pndm(self):

logger = logging.get_logger("diffusers.configuration_utils")

with CaptureLogger(logger) as cap_logger:

pndm = PNDMScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"

)

assert pndm.__class__ == PNDMScheduler

# no warning should be thrown

assert cap_logger.out == ""

def test_overwrite_config_on_load(self):

logger = logging.get_logger("diffusers.configuration_utils")

with CaptureLogger(logger) as cap_logger:

ddpm = DDPMScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch",

subfolder="scheduler",

prediction_type="sample",

beta_end=8,

)

with CaptureLogger(logger) as cap_logger_2:

ddpm_2 = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256", beta_start=88)

with CaptureLogger(logger) as cap_logger:

deprecate("remove this case", "0.13.0", "remove")

ddpm_3 = DDPMScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch",

subfolder="scheduler",

predict_epsilon=False,

beta_end=8,

)

assert ddpm.__class__ == DDPMScheduler

assert ddpm.config.prediction_type == "sample"

assert ddpm.config.beta_end == 8

assert ddpm_2.config.beta_start == 88

assert ddpm_3.config.prediction_type == "sample"

# no warning should be thrown

assert cap_logger.out == ""

assert cap_logger_2.out == ""

def test_load_dpmsolver(self):

logger = logging.get_logger("diffusers.configuration_utils")

with CaptureLogger(logger) as cap_logger:

dpm = DPMSolverMultistepScheduler.from_pretrained(

"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"

)

assert dpm.__class__ == DPMSolverMultistepScheduler

# no warning should be thrown

assert cap_logger.out == ""